Sentences with phrase «from selection bias»

It seems to me the magnitude of the event is important — this maximum is 3 degrees greater than the previous — but this also suffers from selection bias.

For example, it suffers from selection bias, and it treats the model ensemble as a random sample (which it is not).

The parents of New Orleans public school students interviewed by Gross et al. — parents who, resulting from selection bias, are suspected to remain more actively engaged in the school choice process — expressed that they used the resources and valued the information they provided.

Not exact matches

In the early days, Goodman says his company's algorithms inadvertently created a selection bias, favoring applicants from a particular school.

Of course there may be some selection bias there as the people I meet from the rest of the continent are the ones cool enough to take vacations or immigrate.

Even by law: the second one is simply rude language (which you deliberately selected, to contrast with equally biased selection of carefully polite phrase for your side — as if we never hear a rude word from you guys); while the first one is actually a blackmail, despite the said politeness of the form.

With the passage in 1968 of federal legislation mandating decidedly lowered rent ceilings, court rulings limiting discretion in tenant selection, and pressure from the civil rights movement to put an end to racial bias in tenant selection and assignments, this percentage increased.

However, it is unclear to what extent self - selection has biased these studies, as men who remove themselves from a child's residence may differ in many unobserved ways from men who choose to remain.

Except for male sex, which was not a risk factor in study participants, the ORs obtained from participants were similar to those obtained from all eligible cases and controls, providing no evidence of a noticeable selection bias.

That liberals are just as guilty of antiscience bias comports more with accounts of humans chomping canines, and yet those on the left are just as skeptical of well - established science when findings clash with their political ideologies, such as with GMOs, nuclear power, genetic engineering and evolutionary psychology — skepticism of the last I call «cognitive creationism» for its endorsement of a blank - slate model of the mind in which natural selection operated on humans only from the neck down.

In some cases, such as in the selection of participants for studies on suicide, the bias in the original studies may have underestimated the association between access to firearms and suicide, because both study and comparison groups were recruited from health care settings where they may have been seeking treatment for suicidal planning.

The Princeton Review bases its guidebooks on unscientific administrative survey data obtained from colleges as well as surveys of current students which, Reback and Alter say, are notorious among college administrators for selection bias.

We carefully studied issues raised by skeptics: biases from urban heating (we duplicated our results using rural data alone), from data selection (prior groups selected fewer than 20 percent of the available temperature stations; we used virtually 100 percent), from poor station quality (we separately analyzed good stations and poor ones) and from human intervention and data adjustment (our work is completely automated and hands - off).

At day 34 post infection, human CD4 + T cells were purified by positive selection prior to analysis to reduce any bias from low frequency contaminating human cells.

Various manipulations of the data showed that two major potential confounding factors, SNP ascertainment bias and weak selection at presumably - neutral sites, had little influence on the inferences from their data set.

In order to avoid a potential bias caused by selection of a particular referent subject, subject - specific SI maps from all the subjects are summarized as a weighted average, with the Jaccard index for each subject as the weight.

Observational studies have a high risk of bias owing to problems such as self - selection of interventions (people who believe in the benefits of meditation or who have prior experience with meditation are more likely to enroll in a meditation program and report that they benefited from one) and use of outcome measures that can be easily biased by participants» beliefs in the benefits of meditation.

You're using a lot of self - selection bias and outright - incorrect dogma instead of looking at facts and separating confounding from statistics.

And the positive findings for many faith - based programs were tempered with cautions from the authors about selection bias.

Importantly, our null effect estimates from the random experiment differ substantially from those found from an analysis of CPS data, raising concerns about the potential for selection bias in non-experimental estimates of returns.

Evaluations of newer large - scale programs (like those in New Jersey, Oklahoma, and Texas) suffer from «selection bias» problems — we don't know whether the children enrolled in them might be different in important ways from their peers who didn't enroll.

To overcome the bias that results from self - selection into peer groups, our main analysis compares cohorts of students in the same grade at the same school in different years.

They explain that the challenges for researchers are that the school effect must be disaggregated from family background, and that their methods must account for «selection bias» — the likelihood that children whose parents choose a charter school are already different from those whose parents do not.

Results avoid bias from within - country selection and are robust to continental fixed effects and to controlling for non-performance-based forms of teacher salary adjustments.

If differences in follow - up rates are small, selection bias from differential attrition is also likely to be modest.

In the program evaluation literature, this is known as selection bias, and it makes it very hard to infer the impact of a particular program or policy from observational data.

My hypotheses going in to this study is that when first looking at choice schools on student achievement I would see a positive effect because of selection bias; I expected that the students in choice schools would be systematically different from those in traditional public school due to parental factors that affected their selection of a choice program.

Plus it's a highly effective way to separate the (quantitative) stock valuation process from the (more qualitative) stock selection process — and when the companies stand revealed, investors can examine (individually & in aggregate) their stock valuation process & potential biases in a far more detached and objective fashion.

But aside from the more obvious benefits, there's another compelling reason to avoid home bias & embrace diversification — it can actually simplify your stock selection process!

In order to remove this bias from my stock selection I used the following method to pick the stocks.

The authors also acknowledge the limitations of their conclusions given problems arising from differences in market risk and the possibility of selection bias, a common problem also found when examining the performance of hedge funds.

This difference is likely attributable to a selection of bias in terms of which animals are listed on Petfinder, and the fact that certain animals are removed from the website by shelters for reasons that are often unknown to Petfinder (perhaps because they were euthanized).

Only one member from each team, usually in a leadership role, filled out the survey, resulting in a potential selection bias.

I also stumbled on Guy Pearse's lecture on this (below) and find it hard to refute (of course there's selection bias by me because this was my concern from the get - go).

Instead of involving a choice of whether to keep or discard an observation based upon a prior expectation, we hypothesize that this selection bias involves the «survival» of climate models from generation to generation, based upon their warming rate.

And I would offer a similar criticism of that as well, as IMO, you neither ground that form of analogizing in a scientific manner; as I have told you, I think that your inclusion and exclusion criteria selection process is quite arbitrary, and I don't think that it is coincidence that it confirms your distinction of a group you belong to («skeptics») from a group you criticize («realists») in ways that (1) reaffirm a superiority in the group you belong to and, (2) I consider to be superficial and not meaningful as compared to the vastly more important underlying similarities (e.g., the tendency toward identity protective behavior, motivated reasoning, cultural cognition, confirmation bias, emotively - influenced reasoning, etc.)...

My impression from outside is that the statistical analyses are weak, the climate models are simplistic and overinfluenced by selection and publication biases, the theoretic underpinning is extraordinarily shakey and the belief engine is overrevved with the popularity of certain «star performers» and the Romantic desire for a Paradise Lost that never existed.

=========== Unfortunately Trenberth demonstrates selection bias in the quote, which is what separates an activist from a scientists.

Key issues identified in inland and marine presentations included the need to standardize the spatial domain, minimize double counting of emissions from lakes and wetlands, reduce bias in field site selections, improve measurements of cold season emissions, and improve scaling of hot spots.

We carefully studied issues raised by skeptics: biases from urban heating (we duplicated our results using rural data alone), from data selection (prior groups selected fewer than 20 percent of the available temperature stations; we used virtually 100 percent), from poor station quality (we separately analyzed good stations and poor ones) and from human intervention and data adjustment (our work is completely automated and hands - off).

The weighting scheme used to rate stations for the initial selection in the GSN clearly indicates the biases climatologists have in favor of stations that have been in operation for a long time, that are rural, are agricultural research sites, and are distributed throughout the world with increasing density the farther they are away from the tropics.

With fewer ships, drawn from a small selection of countries, any biases (in SST, or NMAT, or cloud cover) are likely to be more pronounced at that time.

In fact the logic of Bayesian Model Averaging goes completely counter to what you are proposing to do, since it is used to neutralize cherry - picking (or «model selection») bias in situations where researchers can pick from an extremely large number of models.

From their description I don't think there is a bias in their sample of scientists, though there is always the possibility of self - selection, where people might be more likely to respond to a survey if it originates from a source who they perceive to be crediFrom their description I don't think there is a bias in their sample of scientists, though there is always the possibility of self - selection, where people might be more likely to respond to a survey if it originates from a source who they perceive to be credifrom a source who they perceive to be credible.

Berkeley Earth also has carefully studied issues raised by skeptics, such as possible biases from urban heating, data selection, poor station quality, and data adjustment.

The selection of 1950 through 2006 significantly biases the outcome of this study because the U.S. was entering a cooling period in the 1960s and 1970s which creates the illusion of unusual subsequent warming from 1980 through 2006.

This male bias in the SSR deviates from the 1 ∶ 1 sex ratio predicted by natural selection [2] and has prompted a large body of research investigating the causal mechanisms underpinning this anomaly.

It has to do with the basic approach of a prior proxy selection that gets away from the HS biases.

I am not certain if self selection refers to bias in sample selection but if so, in the 1996 survey the selection was randomly drawn from membership lists after the affiliation and activity of the respondent had been determined to represent the climate science community.

[Response: this is an interesting survey, and I think I took part... there are three concerns though: (1) self selection bias and (2) possibility of multiple returns from the particularly... avid (3) possibility of returns from non -(climate --RRB- scientists.